
"""
Main Training Pipeline with Alternating Optimization
Implements the two-stage optimization from paper Section 2.5.2
"""

import yaml
from models import KLPSSCFModel
from data import MultimodalDataset, preprocess_ml100k

def load_config(config_path):
    with open(config_path) as f:
        return yaml.safe_load(f)

def main():
    # Load configuration
    model_config = load_config('configs/model_cf_pss.yaml')
    train_config = load_config('configs/train_cold_start.yaml')
    
    # Preprocess data
    data = preprocess_ml100k(
        path='data/ml-100k/u.data',
        cold_start_threshold=train_config['cold_start_threshold']
    )
    
    # Initialize model
    model = KLPSSCFModel(
        num_users=data['train'].shape[0],
        num_items=data['train'].shape[1],
        latent_dim=model_config['latent_dim']
    )
    
    # Alternating optimization loop
    for stage in range(train_config['max_stages']):
        # Stage 1: Update latent representations
        train_latent_space(model, data['train'], train_config)
        
        # Stage 2: Update subgroup clustering
        update_subgroup_structure(model, data['train'])
        
def train_latent_space(model, train_data, config):
    """Fixed subgroup structure, update embeddings"""
    optimizer = torch.optim.Adam(
        model.parameters(), 
        lr=config['learning_rate']
    )
    
    for epoch in range(config['num_epochs']):
        model.train()
        for batch in train_data:
            optimizer.zero_grad()
            pred = model(batch)
            loss = compute_total_loss(pred, batch)
            loss.backward()
            optimizer.step()

def update_subgroup_structure(model, data):
    """Fixed embeddings, optimize clusters"""
    adj_matrix = construct_adjacency(data)
    clusters = spectral_clustering(adj_matrix)
    model.update_clusters(clusters)
